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Hadoop New Batch On 15Th Aug At 6.30 Am(Ist) By Certified Trainer


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[color=#000000][size=6][b]Contact No: [/b][/size][/color][color=#FF0000][size=6][b]+91 9036 298 699[/b][/size][/color][/font][/color]


[color=#282828][font=helvetica, arial, sans-serif][size=6][color=#4B0082][b]Hadoop Demo on 15th Aug at 6.00 AM(IST)[/b][/color][b][color=#4B0082] [/color][/b][color=#4B0082][b]|| 14th Aug at [/b][/color][color=#4B0082][b]at 8.30 PM(EST) [/b][/color][/size][/font][/color][b][size=6]by Hadoop Certified trainer [/size][/b][b][size=6]with case studies[/size][/b]
[color=#000000][font=Helvetica Neue, Arial, Verdana, sans-serif][size=5][color=#222222][b]and 40+ Exercises-Fully Solved. Decks, VM Machine.[/b][/color][/size][/font][/color]

Apache Hadoop Big Data Training Course
Introduction to HADOOP
 Distributed computing , cloud computing
 Big data Basics and Need for Parallel Processing
 How Hadoop works ?
 Introduction to HDFS and Map Reduce
Hadoop Architecture Details
 Name Node
 Data Node
 Secondary Name Node
 Job Tracker
 Task Tracker
HDFS ( Hadoop - Distributed File System)
 Hadoop Distributed file system , Background, GFS
 Data Replication
 Data Storage
 Data Retrieval
 Additional HDFS commands
MapReduce Programming
 MapReduce, Background
 Writing MapReduce Programs
 Writable and WritableComparable
 Input Format, Output Format
 Input Split and Block size
 Combiner
 Partitioner
 Number of Mappers and Reducers
 Counters
Map Reduce Algorithms and Exercises
 Line Count and Word Count
 Distributed Search
 Sorting Data – Key Value Data Type Mathematical Transformation example
 Working with Counters exercise
 Distributed Cache exercise
 Zero Reducer based exercises
Hadoop Streaming
 Introduction to Hadoop Streaming
 Streaming API details and use cases
 Python Based Example for Streaming API
 Exercise for Hadoop Streaming ( XML Files ) Based.
 Exercises on Ruby
 Exercise on C# using MS-Azure.
Apache Pig
 Installation
 Execution Types
 Grunt Shell
 Pig Latin
 Data Processing
 Loading and Storing
 Data Filtering
 Grouping & Joining Operations
 Hands on Exercises
Apache HBase Installation and Details
 HBase and NOSQL Introduction
 HBase Installation and Configuration.
 HBase and Java Based integration
 HBase Hadoop Integration Details.
 Hbase basic exercises
Apache Hive Instalaltion and Details
 Hive Installation on Single cluster Hadoop Node.
 Hive Services
 Hive Shell Description
 Hive Server·
 Meta store Details
 Hive QL Basics Working with Tables, Databases etc.
 Hive JDBC programming
 Hands on Exercises and Assignments
Introduction to Amazon Map Reduce (AWS-EMR)
 Hadoop using Amaozon Web Service
 AWS MapReduce and EC2
 AWS - S3 Service Model.
 AWS-MR Architecture.
 Streaming Exercise using EMR JobFlow.
Hadoop Infrastructure Planning
 Basic Hadoop hardware and software req
 Small , Medium and Large cluster
 Networking challenges in Hadoop Deployment
 Disaster Recovery ( DR ) in Hadoop .
 Performance Tuning a large cluster
Hadoop Industry Solutions
 EMC GreenPlum Introduction
 IBM BigInsight Details
 Oracle , Microsoft etc Hadoop Offerings
 Cloudera and HortonWorks Hadoop Package
Hadoop and Cloud Computing
 Using Cloud technologies for distributed processing
 Hadoop on Amazon Web Service.
 Hadoop in Oracle Cloud / RackSpace
============================================================
Sample Session:

Medium: GotoMeeting
Duration: 35 HoursMaterials: 40+ Exercises-Fully Solved. Decks, VM Machine.

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